Details

Subject(s)

• Industrial organization (Economic theory)—Handbooks, manuals, etc [Browse]
• Industries—Economic aspects [Browse]

Author

• Hortaçsu, Ali [Browse]
• Lizzeri, Alessandro [Browse]

Series

Handbooks in economics. [More in this series]

Source of description

Description based on print version record.

Contents

• Front Cover
• Handbook of Industrial Organization, Volume 4
• Copyright
• Contents
• Contributors
• Introduction to the series
• Preface
• 1 Foundations of demand estimation
• 1 Introduction
• 1.1 Why estimate demand?
• 1.2 Our focus
• 2 The challenges of demand estimation
• 2.1 The first fundamental challenge
• 2.2 The second fundamental challenge
• 2.3 Demand is not regression
• 2.4 A surprisingly difficult case: exogenous prices
• 2.5 Many common tools fall short
• 2.5.1 Controls, including fixed effects
• 2.5.2 Control function
• 2.5.3 Average treatment effects
• 2.6 Balancing flexibility and practicality
• 2.7 Demand or utilities?
• 3 Discrete choice demand
• 3.1 Random utility models
• 3.2 The canonical model
• 3.3 Why random coefficients?
• 4 Market-level data
• 4.1 The BLP estimator
• 4.2 Instruments
• 4.2.1 Cost shifters and their proxies
• 4.2.2 BLP instruments
• 4.2.3 Waldfogel-Fan instruments
• 4.2.4 Exogenous measures of market structure
• 4.2.5 Optimal instruments
• 4.2.6 Evaluating instruments
• 4.3 Using a supply side
• 4.4 Computing the BLP estimator and standard errors
• 5 Nonparametric identification: market-level data
• 5.1 Insights from parametric models
• 5.1.1 Multinomial logit
• 5.1.2 Nested logit
• 5.1.3 The BLP model
• 5.1.4 Index, inversion, and instruments
• 5.2 Nonparametric demand model
• 5.2.1 A nonparametric index
• 5.2.2 Inverting demand
• 5.3 Identification via instruments
• 5.4 Discussion
• 5.4.1 Why 2J instruments?
• 5.4.2 Why BLP instruments?
• 5.4.3 Why the index?
• 5.4.4 Further restrictions and tradeoffs
• 6 Micro data, panels, and ranked choices
• 6.1 Micro data
• 6.2 Consumer panels
• 6.3 Ranked choice data
• 6.4 Hybrids
• 7 Nonparametric identification with micro data
• 7.1 Nonparametric demand model
• 7.2 Identification.
• 7.2.1 Identification of the index function
• 7.2.2 Identification of demand
• 7.3 Discussion
• 8 Some directions for future work
• References
• 2 Empirical models of demand and supply in differentiated products industries
• 2 A motivating example
• 2.1 Model
• 2.1.1 Supply
• 2.1.2 Demand
• 2.2 Estimation and results
• 2.3 Discussion
• 3 Demand
• 3.1 Background
• 3.2 Discrete choice demand models
• 3.2.1 Price elasticity and substitution patterns
• 3.2.2 Consumer welfare
• 4 Demand estimation
• 4.1 The estimation problem
• 4.2 What variation in the data can identify the parameters?
• 4.2.1 Intuition from individual-level data
• 4.2.2 The informational content of E[ξ|Z]=0
• 4.3 The general estimation procedure
• 4.3.1 Instrumental variables
• BLP instruments
• Hausman instruments
• Waldfogel instruments
• 4.3.2 Additional sources of variation
• Multiple markets
• Micro moments and second choice data
• Supply-side moments
• 4.3.3 Efficiency
• 4.3.4 Computational algorithms
• Nested fixed point
• Mathematical programming with equilibrium constraints (MPEC):
• Approximate BLP (ABLP):
• 4.4 Extensions
• 4.4.1 Error in market shares
• 4.4.2 Non-parametric and flexible estimation
• 5 Supply
• 5.1 The workhorse model of horizontal competition
• 5.2 Distinguishing between models of competition
• 5.3 Adding retailers into the mix
• 5.4 Models of bargaining
• 6 Extensions of the demand model
• 6.1 Extensions to the static demand model
• 6.1.1 Multiple goods
• 6.1.2 General characteristics demand models
• 6.2 Dynamic demand
• 6.2.1 Storable products
• 6.2.2 Durable products
• 7 Concluding comments
• 3 An industrial organization perspective on productivity
• 1 A productivity primer
• 1.1 Background and focus
• 1.2 Productivity conceptualized.
• 2 Empirical facts about productivity at the producer level
• 2.1 Dispersion
• 2.2 Persistence within producers
• 2.3 Correlations
• 3 A simple model of equilibrium productivity dispersion
• 3.1 Demand
• 3.2 Supply
• 3.3 Equilibrium
• 3.4 Empirical implications
• 4 Measurement of output and inputs
• 4.1 Output measurement
• 4.2 Input measurement
• 4.3 Data sources
• 5 Recovering productivity from the data
• 5.1 Operating environment and unit of analysis
• 5.1.1 Market structure
• 5.1.2 Unit of analysis
• 5.1.3 Output and input data
• 5.1.4 Trade-offs across approaches
• 5.1.5 Notation and setup
• 5.2 Factor shares
• 5.3 Production function estimation (producer level)
• 5.3.1 Perfect competition (A.1)
• Control function approach
• Selection bias
• Procedure
• Dynamic panel
• Discussion
• 5.3.2 Imperfect competition (B.1)
• Homogeneous good
• Product differentiation
• Deflating revenue
• Adding demand-side information
• Pass-through
• Beyond price data: how to compare quantities?
• 5.3.3 Impact on the coefficients of interest
• 5.4 Multi-product production
• 5.4.1 Allocation of inputs to products
• Explicit aggregation from product to producer level
• 5.4.2 Estimate transformation function (A.2)
• 5.4.3 Product differentiation and imperfect competition (B.2.2)
• Illustration
• 5.5 Cost versus production functions
• 5.6 Measurement and specification errors
• 5.6.1 Measurement error
• 5.6.2 Model misspecification
• Productivity process
• Technology heterogeneity
• Functional form
• 6 Productivity analysis
• 6.1 Producer-level productivity analysis
• 6.1.1 Identifying producer-level drivers
• Exogenous drivers
• Endogenous drivers
• 6.1.2 Sources of productivity differences
• Managerial practices
• Unobservable input quality
• Intangible capital
• Firm structure
• Product-side differences.
• 6.2 Aggregate analysis: resource (re/mis)allocation
• 6.2.1 What does theory predict?
• 6.2.2 Empirical work
• Decomposing industry aggregate productivity
• 6.2.3 Exogenous drivers: reallocation
• Deregulation
• Technology
• 6.2.4 Endogenous drivers and aggregation: market power
• 6.3 Misallocation
• 7 Looking ahead
• 7.1 Market power and productivity data
• 7.1.1 Measuring market power using production data
• Applications
• 7.1.2 Integrating product and factor markets using productivity data
• Vertical linkages
• Labor market power
• 7.2 Technological change and market-level outcomes
• 7.2.1 Factor-biased technological change
• 7.2.2 Endogenous productivity growth
• 8 Conclusion
• 4 Dynamic games in empirical industrial organization
• 1.1 Role of dynamic games in empirical industrial organization
• 1.2 Organization of this chapter
• 2 Models
• 2.1 Basic framework
• 2.2 Markov perfect Nash equilibrium
• 2.2.1 Definition
• 2.2.2 Equilibrium existence
• 2.2.3 Incomplete information
• 2.2.4 Multiple equilibria
• 2.3 Examples
• 2.4 Extensions of the basic framework
• 2.4.1 Continuous time
• 2.4.2 Oblivious equilibrium
• 2.4.3 Large state spaces
• 2.4.4 Persistent asymmetric information
• 2.4.5 Firms' biased beliefs
• 3 Identification and estimation
• 3.1 Data
• 3.2 Identification
• 3.2.1 Non-identification result
• 3.2.2 A set of sufficient conditions for identification
• 3.2.3 Relaxing restrictions (ID.1) to (ID.8)
• 3.2.4 Identification of mixed continuous-discrete choice models
• 3.3 Estimation methods
• 3.3.1 Full solution methods
• 3.3.2 Two-step CCP methods
• 3.3.3 Bajari-Benkard-Levin (BBL) method
• 3.3.4 Large state space and finite dependence
• 3.3.5 Unobserved market heterogeneity
• 3.4 The promise of machine learning
• 4 Empirical applications.
• 4.1 Earlier empirical work on dynamic games
• 4.1.1 Competition in the hospital market
• 4.1.2 Dynamic output competition with learning by doing
• 4.1.3 Dynamics in auctions
• 4.1.4 Environmental regulations in concentrated industries
• 4.1.5 Demand shocks and market structure
• 4.1.6 Subsidizing entry
• 4.2 Innovation and market structure
• 4.2.1 Microprocessor innovation: Intel vs AMD
• 4.2.2 Hard drive innovation: new products and cannibalization
• 4.2.3 Car innovation and quality ladders
• 4.2.4 Data on innovation
• 4.3 Antitrust policy towards mergers
• 4.3.1 Endogenous mergers
• 4.3.2 Evolving market structure and mergers
• 4.3.3 Revealed merger efficiencies
• 4.4 Dynamic pricing
• 4.4.1 Competition with price adjustment costs
• 4.4.2 Limit pricing
• 4.4.3 Dynamic pricing with network effects
• 4.5 Regulation
• 4.5.1 Environmental regulation
• 4.5.2 Land use regulation
• 4.5.3 Product variety
• 4.5.4 Industrial policy
• 4.6 Retail
• 4.6.1 Economies of density and cannibalization
• 4.6.2 Chains
• 4.6.3 Unobserved heterogeneity and entry in retail
• 4.6.4 Effect of Walmart on rival grocers
• 4.6.5 Exit in declining industries
• 4.6.6 Repositioning
• 4.6.7 Advertising
• 4.7 Uncertainty and firms' investment decisions
• 4.7.1 Firm investment under uncertainty
• 4.7.2 Uncertainty and oil drilling in Texas
• 4.7.3 Uncertainty in shipping
• 4.8 Network competition in the airline industry
• 4.9 Dynamic matching
• 4.10 Natural resources
• 5 Concluding remarks
• 5 Moment inequalities and partial identification in industrial organization
• 2 Definitions and background
• 3 Revealed preference
• 3.1 Primitive assumptions
• 3.2 Paths to estimators
• 3.3 Examples
• 3.3.1 Richer assumptions on disturbances
• 4 Generalized discrete choice approaches.
• 4.1 Models of discrete games with complete information.

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ISBN

0-323-91514-0

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